Using Big Data to Redesign the Health System

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Health services are working to develop new models for care that keep people out of hospital, such as community outreach, daily outpatient assessment and hospital in the home. Identifying patient subgroups ('phenotypes') with distinct characteristics that are predictive of their subsequent outcomes (e.g. admission, complications) is key to designing these new models. Machine learning (ML) techniques are data-driven approaches that can discover statistical patterns in high-dimensional, multivariate data sets. This project will apply ML techniques to health 'big data' to identify patient phenotypes that will support the design and implementation of new tailored care pathways for patients with chronic disease.

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Supervisory team

Centre for Big Data Research in Health

Mathematics and Statistics

Centre for Big Data Research in Health

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